2024
Thomae, Martha E.; Rizo, David; Fuentes-Martínez, Eliseo; Raurich, Cristina Alís; Luca, Elsa De; Calvo-Zaragoza, Jorge
A Preliminary Proposal for a Systematic GABC Encoding of Gregorian Chant Proceedings Article
In: ACM International Conference Proceeding Series, pp. 45-53, Association for Computing Machinery, 2024, ISBN: 9798400717208.
Abstract | Links | BibTeX | Tags: Aquitanian neumes, GABC, Gregorian chant, MEI, music encoding, Plainchant, REPERTORIUM, square notation
@inproceedings{Thomae2024,
title = {A Preliminary Proposal for a Systematic GABC Encoding of Gregorian Chant},
author = {Martha E. Thomae and David Rizo and Eliseo Fuentes-Martínez and Cristina Alís Raurich and Elsa De Luca and Jorge Calvo-Zaragoza},
doi = {10.1145/3660570.3660581},
isbn = {9798400717208},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {ACM International Conference Proceeding Series},
pages = {45-53},
publisher = {Association for Computing Machinery},
abstract = {In the last years, several approaches have addressed the encoding of the different music scripts used for plainchant. One of these approaches is the GABC format. While being a comprehensive symbolic representation of square notation, the lack of a formal specification for GABC usually leads to ambiguities, which must be avoided in the specification of any encoding format. Sometimes, the simple trial-and-error approach of entering the GABC code into an engraving system - such as Illuminare, Scrib.io, or GABC Transcription Tool - can solve this ambiguity. However, these engraving systems have shown some inconsistency among themselves when rendering GABC, sometimes displaying different music for the same code snippet. This paper presents a systematic approach to encoding Gregorian chant originally written in Aquitanian neumes and square notation to eliminate ambiguities inherent in the GABC specification. By formalizing the grammar of GABC, we address the challenges of inaccurate renderings in current music notation software. Our methodology includes developing a "Systematic GABC"(S-GABC) following a critical and scientific mentality to ensure the endurance of the notation. This paper demonstrates our system's effectiveness in standardizing Gregorian chant encoding, offering significant contributions to digital musicology and enhancing the accuracy of musical heritage digitization.},
keywords = {Aquitanian neumes, GABC, Gregorian chant, MEI, music encoding, Plainchant, REPERTORIUM, square notation},
pubstate = {published},
tppubtype = {inproceedings}
}
In the last years, several approaches have addressed the encoding of the different music scripts used for plainchant. One of these approaches is the GABC format. While being a comprehensive symbolic representation of square notation, the lack of a formal specification for GABC usually leads to ambiguities, which must be avoided in the specification of any encoding format. Sometimes, the simple trial-and-error approach of entering the GABC code into an engraving system - such as Illuminare, Scrib.io, or GABC Transcription Tool - can solve this ambiguity. However, these engraving systems have shown some inconsistency among themselves when rendering GABC, sometimes displaying different music for the same code snippet. This paper presents a systematic approach to encoding Gregorian chant originally written in Aquitanian neumes and square notation to eliminate ambiguities inherent in the GABC specification. By formalizing the grammar of GABC, we address the challenges of inaccurate renderings in current music notation software. Our methodology includes developing a "Systematic GABC"(S-GABC) following a critical and scientific mentality to ensure the endurance of the notation. This paper demonstrates our system's effectiveness in standardizing Gregorian chant encoding, offering significant contributions to digital musicology and enhancing the accuracy of musical heritage digitization.2023
Martínez-Sevilla, J. C.; Ríos-Vila, A.; Castellanos, F. J.; Calvo-Zaragoza, J.
A Holistic Approach for Aligned Music and Lyrics Transcription Conference
Document Analysis and Recognition - ICDAR 2023, vol. 1, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-41676-7.
Abstract | Links | BibTeX | Tags: REPERTORIUM
@conference{MartinezSevilla:ICDAR:2023,
title = {A Holistic Approach for Aligned Music and Lyrics Transcription},
author = {J.C. Martínez-Sevilla and A. Ríos-Vila and F. J. Castellanos and J. Calvo-Zaragoza },
editor = {Fink, Gernot A. and Jain, Rajiv and Kise, Koichi and Zanibbi, Richard},
doi = {https://doi.org/10.1007/978-3-031-41676-7_11},
isbn = {978-3-031-41676-7},
year = {2023},
date = {2023-08-28},
urldate = {2023-08-28},
booktitle = {Document Analysis and Recognition - ICDAR 2023},
volume = {1},
pages = {185--201},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {In this paper, we present the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, whose goal is to retrieve the content from document images of vocal music. This new research area arises from the need to automatically transcribe notes and lyrics from music scores and align both sources of information conveniently. Although existing methods are able to deal with music notation and text, they work without providing their proper alignment, which is crucial to actually retrieve the content of the piece of vocal music. To overcome this challenge, we consider holistic neural approaches that transcribe music and text in one step, along with an encoding that implicitly aligns the sources of information. The methodology is evaluated on a benchmark specifically designed for AMNLT. The results report that existing methods can obtain high-quality text and music transcriptions, but posterior alignment errors are inevitably found. However, our formulation achieves relative improvements of over 80{%} in the metric that considers both transcription and alignment. We hope that this work will establish itself as a future reference for further research on AMNLT.},
keywords = {REPERTORIUM},
pubstate = {published},
tppubtype = {conference}
}
In this paper, we present the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, whose goal is to retrieve the content from document images of vocal music. This new research area arises from the need to automatically transcribe notes and lyrics from music scores and align both sources of information conveniently. Although existing methods are able to deal with music notation and text, they work without providing their proper alignment, which is crucial to actually retrieve the content of the piece of vocal music. To overcome this challenge, we consider holistic neural approaches that transcribe music and text in one step, along with an encoding that implicitly aligns the sources of information. The methodology is evaluated on a benchmark specifically designed for AMNLT. The results report that existing methods can obtain high-quality text and music transcriptions, but posterior alignment errors are inevitably found. However, our formulation achieves relative improvements of over 80{%} in the metric that considers both transcription and alignment. We hope that this work will establish itself as a future reference for further research on AMNLT.
2024
Thomae, Martha E.; Rizo, David; Fuentes-Martínez, Eliseo; Raurich, Cristina Alís; Luca, Elsa De; Calvo-Zaragoza, Jorge
A Preliminary Proposal for a Systematic GABC Encoding of Gregorian Chant Proceedings Article
In: ACM International Conference Proceeding Series, pp. 45-53, Association for Computing Machinery, 2024, ISBN: 9798400717208.
Abstract | Links | BibTeX | Tags: Aquitanian neumes, GABC, Gregorian chant, MEI, music encoding, Plainchant, REPERTORIUM, square notation
@inproceedings{Thomae2024,
title = {A Preliminary Proposal for a Systematic GABC Encoding of Gregorian Chant},
author = {Martha E. Thomae and David Rizo and Eliseo Fuentes-Martínez and Cristina Alís Raurich and Elsa De Luca and Jorge Calvo-Zaragoza},
doi = {10.1145/3660570.3660581},
isbn = {9798400717208},
year = {2024},
date = {2024-01-01},
urldate = {2024-01-01},
booktitle = {ACM International Conference Proceeding Series},
pages = {45-53},
publisher = {Association for Computing Machinery},
abstract = {In the last years, several approaches have addressed the encoding of the different music scripts used for plainchant. One of these approaches is the GABC format. While being a comprehensive symbolic representation of square notation, the lack of a formal specification for GABC usually leads to ambiguities, which must be avoided in the specification of any encoding format. Sometimes, the simple trial-and-error approach of entering the GABC code into an engraving system - such as Illuminare, Scrib.io, or GABC Transcription Tool - can solve this ambiguity. However, these engraving systems have shown some inconsistency among themselves when rendering GABC, sometimes displaying different music for the same code snippet. This paper presents a systematic approach to encoding Gregorian chant originally written in Aquitanian neumes and square notation to eliminate ambiguities inherent in the GABC specification. By formalizing the grammar of GABC, we address the challenges of inaccurate renderings in current music notation software. Our methodology includes developing a "Systematic GABC"(S-GABC) following a critical and scientific mentality to ensure the endurance of the notation. This paper demonstrates our system's effectiveness in standardizing Gregorian chant encoding, offering significant contributions to digital musicology and enhancing the accuracy of musical heritage digitization.},
keywords = {Aquitanian neumes, GABC, Gregorian chant, MEI, music encoding, Plainchant, REPERTORIUM, square notation},
pubstate = {published},
tppubtype = {inproceedings}
}
In the last years, several approaches have addressed the encoding of the different music scripts used for plainchant. One of these approaches is the GABC format. While being a comprehensive symbolic representation of square notation, the lack of a formal specification for GABC usually leads to ambiguities, which must be avoided in the specification of any encoding format. Sometimes, the simple trial-and-error approach of entering the GABC code into an engraving system - such as Illuminare, Scrib.io, or GABC Transcription Tool - can solve this ambiguity. However, these engraving systems have shown some inconsistency among themselves when rendering GABC, sometimes displaying different music for the same code snippet. This paper presents a systematic approach to encoding Gregorian chant originally written in Aquitanian neumes and square notation to eliminate ambiguities inherent in the GABC specification. By formalizing the grammar of GABC, we address the challenges of inaccurate renderings in current music notation software. Our methodology includes developing a "Systematic GABC"(S-GABC) following a critical and scientific mentality to ensure the endurance of the notation. This paper demonstrates our system's effectiveness in standardizing Gregorian chant encoding, offering significant contributions to digital musicology and enhancing the accuracy of musical heritage digitization.
2023
Martínez-Sevilla, J. C.; Ríos-Vila, A.; Castellanos, F. J.; Calvo-Zaragoza, J.
A Holistic Approach for Aligned Music and Lyrics Transcription Conference
Document Analysis and Recognition - ICDAR 2023, vol. 1, Springer Nature Switzerland, Cham, 2023, ISBN: 978-3-031-41676-7.
Abstract | Links | BibTeX | Tags: REPERTORIUM
@conference{MartinezSevilla:ICDAR:2023,
title = {A Holistic Approach for Aligned Music and Lyrics Transcription},
author = {J.C. Martínez-Sevilla and A. Ríos-Vila and F. J. Castellanos and J. Calvo-Zaragoza },
editor = {Fink, Gernot A. and Jain, Rajiv and Kise, Koichi and Zanibbi, Richard},
doi = {https://doi.org/10.1007/978-3-031-41676-7_11},
isbn = {978-3-031-41676-7},
year = {2023},
date = {2023-08-28},
urldate = {2023-08-28},
booktitle = {Document Analysis and Recognition - ICDAR 2023},
volume = {1},
pages = {185--201},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {In this paper, we present the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, whose goal is to retrieve the content from document images of vocal music. This new research area arises from the need to automatically transcribe notes and lyrics from music scores and align both sources of information conveniently. Although existing methods are able to deal with music notation and text, they work without providing their proper alignment, which is crucial to actually retrieve the content of the piece of vocal music. To overcome this challenge, we consider holistic neural approaches that transcribe music and text in one step, along with an encoding that implicitly aligns the sources of information. The methodology is evaluated on a benchmark specifically designed for AMNLT. The results report that existing methods can obtain high-quality text and music transcriptions, but posterior alignment errors are inevitably found. However, our formulation achieves relative improvements of over 80{%} in the metric that considers both transcription and alignment. We hope that this work will establish itself as a future reference for further research on AMNLT.},
keywords = {REPERTORIUM},
pubstate = {published},
tppubtype = {conference}
}
In this paper, we present the Aligned Music Notation and Lyrics Transcription (AMNLT) challenge, whose goal is to retrieve the content from document images of vocal music. This new research area arises from the need to automatically transcribe notes and lyrics from music scores and align both sources of information conveniently. Although existing methods are able to deal with music notation and text, they work without providing their proper alignment, which is crucial to actually retrieve the content of the piece of vocal music. To overcome this challenge, we consider holistic neural approaches that transcribe music and text in one step, along with an encoding that implicitly aligns the sources of information. The methodology is evaluated on a benchmark specifically designed for AMNLT. The results report that existing methods can obtain high-quality text and music transcriptions, but posterior alignment errors are inevitably found. However, our formulation achieves relative improvements of over 80{%} in the metric that considers both transcription and alignment. We hope that this work will establish itself as a future reference for further research on AMNLT.